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  <title>识别小车身上的特定亮条 | 鲨鱼之家</title>
  <meta name="description" content="任务描述 代码   任务描述 使用OpenCV-python读取image_test.png，经过一定处理，框出指定亮条。image_test.png如下图所示（可以右键单击获取图片路径，获得图片）：  输出图片应该如下图所示：  代码 （使用的是python2） 12345678910111213141516171819202122232425262728293031323334353637">
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<meta property="og:description" content="任务描述 代码   任务描述 使用OpenCV-python读取image_test.png，经过一定处理，框出指定亮条。image_test.png如下图所示（可以右键单击获取图片路径，获得图片）：  输出图片应该如下图所示：  代码 （使用的是python2） 12345678910111213141516171819202122232425262728293031323334353637">
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<li><a href="#%E4%BB%BB%E5%8A%A1%E6%8F%8F%E8%BF%B0">任务描述</a></li>
<li><a href="#%E4%BB%A3%E7%A0%81">代码</a></li>
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<h2><span id="任务描述">任务描述</span></h2>
<p>使用OpenCV-python读取image_test.png，经过一定处理，框出指定亮条。image_test.png如下图所示（可以右键单击获取图片路径，获得图片）：</p>
<p><img src="https://gitee.com/tina-yao/bigbig-shark/raw/master/imgs/car_light_bar/image_test.png" alt="image_test.png"></p>
<p>输出图片应该如下图所示：</p>
<p><img src="https://gitee.com/tina-yao/bigbig-shark/raw/master/imgs/car_light_bar/image_output.png" alt="image_output.png"></p>
<h2><span id="代码">代码</span></h2>
<p>（使用的是python2）</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span class="line">30</span><br><span class="line">31</span><br><span class="line">32</span><br><span class="line">33</span><br><span class="line">34</span><br><span class="line">35</span><br><span class="line">36</span><br><span class="line">37</span><br><span class="line">38</span><br><span class="line">39</span><br><span class="line">40</span><br><span class="line">41</span><br><span class="line">42</span><br><span class="line">43</span><br><span class="line">44</span><br><span class="line">45</span><br><span class="line">46</span><br><span class="line">47</span><br><span class="line">48</span><br><span class="line">49</span><br><span class="line">50</span><br><span class="line">51</span><br><span class="line">52</span><br><span class="line">53</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment">#!/usr/bin/python</span></span><br><span class="line"><span class="comment">#encoding:utf-8</span></span><br><span class="line"><span class="keyword">import</span> cv2</span><br><span class="line"><span class="keyword">import</span> numpy <span class="keyword">as</span> np</span><br><span class="line"><span class="string">&#x27;&#x27;&#x27;https://www.projectpro.io/recipes/detect-specific-colors-from-image-opencv&#x27;&#x27;&#x27;</span></span><br><span class="line"></span><br><span class="line">img_path = <span class="string">&quot;image_test.png&quot;</span></span><br><span class="line"></span><br><span class="line"><span class="comment"># define a lower limit and an upper limit of BGR values of the blue</span></span><br><span class="line">lower_blue = np.array([<span class="number">205</span>, <span class="number">205</span>, <span class="number">104</span>], dtype=<span class="string">&quot;uint8&quot;</span>)</span><br><span class="line">upper_blue = np.array([<span class="number">255</span>, <span class="number">255</span>, <span class="number">255</span>], dtype=<span class="string">&quot;uint8&quot;</span>)</span><br><span class="line"></span><br><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">process_img</span>():</span></span><br><span class="line">    <span class="comment"># read</span></span><br><span class="line">    img = cv2.imread(img_path)</span><br><span class="line"></span><br><span class="line">    <span class="comment"># erode</span></span><br><span class="line">    copy = img.copy()</span><br><span class="line">    mask1 = cv2.erode(copy, <span class="literal">None</span>, iterations=<span class="number">3</span>) <span class="comment"># 3 is better, i is also ok</span></span><br><span class="line">    <span class="comment">#cv2.imshow(&quot;Eroded_img&quot;, mask1)</span></span><br><span class="line">    <span class="comment">#cv2.waitKey(0)</span></span><br><span class="line"></span><br><span class="line">    <span class="comment"># range</span></span><br><span class="line">    mask = cv2.inRange(mask1, lower_blue, upper_blue)</span><br><span class="line">    detected_output = cv2.bitwise_and(mask1, mask1, mask=mask)</span><br><span class="line">    <span class="comment">#cv2.imshow(&quot;Detected_output&quot;, detected_output)</span></span><br><span class="line">    <span class="comment">#cv2.waitKey(0)</span></span><br><span class="line"></span><br><span class="line">    <span class="comment"># gray</span></span><br><span class="line">    gray_img = cv2.cvtColor(detected_output, cv2.COLOR_BGR2GRAY)</span><br><span class="line"></span><br><span class="line">    <span class="comment"># thresh</span></span><br><span class="line">    thresh_img = cv2.threshold(gray_img, <span class="number">200</span>, <span class="number">255</span>, cv2.THRESH_BINARY)[<span class="number">1</span>]</span><br><span class="line">    <span class="comment">#cv2.imshow(&quot;Thresh_img&quot;, thresh_img)</span></span><br><span class="line">    <span class="comment">#cv2.waitKey(0)</span></span><br><span class="line"></span><br><span class="line">    <span class="comment"># cv2.findContours 轮廓检测（必须是灰度图） 使用膨胀后的图像做轮廓检测</span></span><br><span class="line">    binary,contours,hierarchy = cv2.findContours(thresh_img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)</span><br><span class="line">    <span class="comment">#print(len(contours))</span></span><br><span class="line"></span><br><span class="line">    <span class="comment"># Maximum circumscribed rectangle</span></span><br><span class="line">    output = img.copy()</span><br><span class="line">    x0, y0, w0, h0 = cv2.boundingRect(contours[<span class="number">0</span>])</span><br><span class="line">    bias = <span class="number">10</span></span><br><span class="line">    cv2.rectangle(output, (x0-bias, y0-bias), (x0+w0+bias, y0+h0+bias), (<span class="number">0</span>, <span class="number">0</span>, <span class="number">255</span>), <span class="number">4</span>)</span><br><span class="line">    x1, y1, w1, h1 = cv2.boundingRect(contours[<span class="number">1</span>])</span><br><span class="line">    cv2.rectangle(output, (x1-bias, y1-bias), (x1+w1+bias, y1+h1+bias), (<span class="number">0</span>, <span class="number">0</span>, <span class="number">255</span>), <span class="number">4</span>)</span><br><span class="line">    cv2.imshow(<span class="string">&quot;Contours&quot;</span>, output)</span><br><span class="line">    cv2.waitKey(<span class="number">0</span>)</span><br><span class="line">    <span class="comment">#cv2.imwrite(&#x27;image_output.png&#x27;, output)</span></span><br><span class="line"></span><br><span class="line"><span class="keyword">if</span> __name__ == <span class="string">&#x27;__main__&#x27;</span>:</span><br><span class="line">    process_img()</span><br></pre></td></tr></table></figure>

      
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